Biblio

Abstract—In this work, we study the problem of keeping the objective functions of individual agents "-differentially private in cloud-based distributed optimization, where agents are subject to global constraints and seek to minimize local objective functions. The communication architecture between agents is cloud-based – instead of communicating directly with each other, they oordinate by sharing states through a trusted cloud computer. In this problem, the difficulty is twofold: the objective functions are used repeatedly in every iteration, and the influence of  erturbing them extends to other agents and lasts over time. To solve the problem, we analyze the propagation of perturbations on objective functions over time, and derive an upper bound on them. With the upper bound, we design a noise-adding mechanism that randomizes the cloudbased distributed optimization algorithm to keep the individual objective functions "-differentially private. In addition, we study the trade-off between the privacy of objective functions and the performance of the new cloud-based distributed optimization algorithm with noise. We present simulation results to numerically verify the theoretical results presented.

Hadoop has become increasingly popular as it rapidly processes data in parallel. Cloud computing gives reli- ability, flexibility, scalability, elasticity and cost saving to cloud users. Deploying Hadoop in cloud can benefit Hadoop users. Our evaluation exhibits that various internal cloud attacks can bypass current Hadoop security mechanisms, and compromised Hadoop components can be used to threaten overall Hadoop. It is urgent to improve compromise resilience, Hadoop can maintain a relative high security level when parts of Hadoop are compromised. Hadoop has two vulnerabilities that can dramatically impact its resilience. The vulnerabilities are the overloaded authentication key, and the lack of fine-grained access control at the data access level. We developed a security enhancement for a public cloud-based Hadoop, named SEHadoop, to improve the compromise resilience through enhancing isolation among Hadoop components and enforcing least access privilege for Hadoop processes. We have implemented the SEHadoop model, and demonstrated that SEHadoop fixes the above vulnerabilities with minimal or no run-time overhead, and effectively resists related attacks.

Concurrent programs are prone to various classes of difficult-to- detect faults, of which data races are particularly prevalent. Prior work has attempted to increase the cost-effectiveness of approaches for testing for data races by employing race detection techniques, but to date, no work has considered cost-effective approaches for re-testing for races as programs evolve. In this paper we present SIMRT, an automated regression testing framework for use in de- tecting races introduced by code modifications. SIMRT employs a regression test selection technique, focused on sets of program ele- ments related to race detection, to reduce the number of test cases that must be run on a changed program to detect races that occur due to code modifications, and it employs a test case prioritiza- tion technique to improve the rate at which such races are detected. Our empirical study of SIMRT reveals that it is more efficient and effective for revealing races than other approaches, and that its con- stituent test selection and prioritization components each contribute to its performance.

When multiple apps on an Android platform interact, faults and security vulnerabilities can occur. Software engineers need to be able to analyze interacting apps to detect such problems. Current approaches for performing such analyses, however, do not scale to the numbers of apps that may need to be considered, and thus, are impractical for application to realworld scenarios. In this paper, we introduce JITANA, a program analysis framework designed to analyze multiple Android apps simultaneously. By using a classloader-based approach instead of a compiler-based approach such as SOOT, JITANA is able to simultaneously analyze large numbers of interacting apps, perform on-demand analysis of large libraries, and effectively analyze dynamically generated code. Empirical studies of JITANA show that it is substantially more efficient than a state-of-theart approach, and that it can effectively and efficiently analyze complex apps including Facebook, Pokemon Go, and Pandora ´ that the state-of-the-art approach cannot handle.